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Advances in Info-MetricsInformation and Information Processing across Disciplines$
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Min Chen, J. Michael Dunn, Amos Golan, and Aman Ullah

Print publication date: 2020

Print ISBN-13: 9780190636685

Published to Oxford Scholarship Online: December 2020

DOI: 10.1093/oso/9780190636685.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 21 June 2021

Forecasting Socioeconomic Distributions on Small-Area Spatial Domains for Count Data

Forecasting Socioeconomic Distributions on Small-Area Spatial Domains for Count Data

Chapter:
(p.240) 9 Forecasting Socioeconomic Distributions on Small-Area Spatial Domains for Count Data
Source:
Advances in Info-Metrics
Author(s):

Rosa Bernardini Papalia

Esteban Fernandez-Vazquez

Publisher:
Oxford University Press
DOI:10.1093/oso/9780190636685.003.0009

Statistical information for empirical analysis is frequently available at a higher level of aggregation than is desired. The spatial disaggregation of the socioeconomic data is considered complex due to the inherent spatial properties and relationships of the spatial data, namely, spatial dependence and spatial heterogeneity. The spatial dependence, spatial heterogeneity, and effect of scale produce major technical issues that largely impact the accuracy of the regional forecast disaggregation. In this chapter, we propose entropy-based spatial forecast disaggregation methods for count areal data that use all available information at each level of aggregation even if it is incomplete. The proposed methods are validated through Monte Carlo simulations using ancillary information. An empirical application to real data is also presented.

Keywords:   count data models, generalized cross entropy, spatial disaggregated regional data

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